1. Field of the Invention
The present invention relates to the field of image processing systems, and more particularly, to an image input processing system for an artificial neural network.
2. Art Background
Artificial neural networks (ANNs) are used in a variety of pattern recognition, optimization and control applications. In ANN pattern recognition applications, a subject image or pattern image is input to a first input layer of the ANN. In conventional ANNs, traditional image processing techniques are employed to extract features from an input pattern. The extracted features are input to any of a variety of ANN paradigms which learn to recognize the input patterns. The ANN paradigms use conventional image processing techniques to extract features from an image. For example, the ANN may extract a center of mass of a shaded area, or moments about a particular axis of the input pattern. Upon extracting the desired pattern features, the extracted features are presented to the network for further processing. Under the feature extraction technique, the ANN becomes dependent on the specific features presented to it by the input system for proper pattern recognition. The feature extraction technique is limited because the ANN does not extract its own features. Instead, the ANN becomes dependent on the particular features provided by conventional techniques.
As an alternative technique to feature extraction, actual pattern data is directly input to the ANN for recognition. In this configuration, the ANN uses the actual pattern data as inputs to respond to a particular image pattern presented to input neurons of the ANN. Because each pixel of the input image represents a specific feature to the ANN, the image presented to these conventional ANNs must always be presented in the same position, orientation and scale as the training image was presented in order for recognition to occur. Therefore, pattern recognition ANNs are very sensitive to any spatial transformation the subject image may undergo relative to the image used for training. To help reduce this spatial transformation problem, ANNs often operate on small images. The small images are presented in the same orientation, scale and position to the ANN so that spatial transformations of the subject image is reduced or limited. The input of actual pattern data has one advantage over feature extraction: the network extracts its own features from the input image. However, the actual pattern input configuration is limited because it depends on a highly structured input environment.